Introduction

Gemini 1.5 Pro is a state-of-the-art multimodal language model developed by Google AI, designed for efficiency and scalability across various tasks. It boasts impressive capabilities, including a groundbreaking long-context understanding feature and performance comparable to much larger models.

Gemini 1.5 Pro Model

This mid-size model leverages a Mixture of Experts (MoE) architecture, enabling it to activate only relevant parts of its neural network based on the input. This selective activation significantly enhances efficiency compared to traditional transformer models. Gemini 1.5 Pro's key innovation lies in its extended context window, capable of processing up to 1 million tokens, allowing it to handle vast amounts of information like lengthy videos, audio recordings, extensive codebases, or large documents.

• Mid-size Model: Optimized for efficiency and scalability, achieving performance comparable to larger models like Gemini 1.0 Ultra.
• Multimodal Understanding: Processes and understands information from various modalities, including text, code, images, audio, and video.
• Long-Context Understanding: Features an experimental long-context window, capable of handling up to 1 million tokens, enabling the processing of extensive information like lengthy documents, codebases, or audio recordings.
• Highly Efficient Architecture: Utilizes Mixture of Experts (MoE) architecture, allowing for selective activation of relevant neural network pathways, significantly improving efficiency and training speed.

Run Gemini 1.5 Pro with an API

Running the API with Clarifai's Python SDK

You can run the Gemini 1.5 Pro Model API using Clarifai’s Python SDK.

Export your PAT as an environment variable. Then, import and initialize the API Client.

Find your PAT in your security settings.

export CLARIFAI_PAT={your personal access token}

Predict via Image URL

from clarifai.client.model import Model
from clarifai.client.input import Inputs

prompt = "What time of day is it?"
image_url = "https://samples.clarifai.com/metro-north.jpg"
inference_params = dict(temperature=0.2, top_k =50, top_p=0.95, max_tokens=100)

model_prediction = Model("https://clarifai.com/gcp/generate/models/gemini-1_5-pro").predict(inputs = [Inputs.get_multimodal_input(input_id="",image_url=image_url, raw_text=prompt)],inference_params=inference_params)

print(model_prediction.outputs[0].data.text.raw)

Predict via local Image

from clarifai.client.model import Model
from clarifai.client.input import Inputs

IMAGE_FILE_LOCATION = 'LOCAL IMAGE PATH'
with open(IMAGE_FILE_LOCATION, "rb") as f:
file_bytes = f.read()


prompt = "What time of day is it?"
inference_params = dict(temperature=0.2, top_k =50, top_p=0.95, max_tokens=100)

model_prediction = Model("https://clarifai.com/gcp/generate/models/gemini-1_5-pro").predict(inputs = [Inputs.get_multimodal_input(input_id="", image_bytes = file_bytes, raw_text=prompt)], inference_params=inference_params)
print(model_prediction.outputs[0].data.text.raw)

Use Cases

• Complex Information Analysis: Analyze, summarize, and classify large volumes of content, such as lengthy documents or transcripts.
• Multimodal Reasoning: Understand and reason across different modalities, extracting insights and connections between text, images, audio, and video.
• In-Context Learning: Learn new skills and tasks directly from prompts, without the need for additional fine-tuning, enabling adaptability to specific situations and domains.
• Code Understanding and Generation: Process and generate code, supporting tasks like code completion, translation, and bug detection.

Evaluation

• Benchmarks: Outperforms Gemini 1.0 Pro on 87% of benchmarks and demonstrates performance comparable to Gemini 1.0 Ultra.
• Long-Context Performance: Maintains high accuracy even with extensive context windows, achieving 99% success in finding specific information within 1 million token blocks.
• In-Context Learning: Shows impressive ability to learn new skills, achieving human-level performance in tasks like translating languages based on provided grammar rules.

Dataset

The specific datasets used for training Gemini 1.5 Pro are not publicly available. However, it is likely trained on a massive dataset of text and code, combined with data from various modalities such as images, audio, and video.

Advantages

• Efficiency: Achieves high performance with a smaller model size, enabling faster training and inference.
• Versatility: Handles various tasks across different modalities, providing a comprehensive AI solution.
• Long-Context Understanding: Processes and understands extensive information, enabling deeper insights and analysis.
• In-Context Learning: Adapts to new tasks and domains without requiring additional training.

Limitations

• Bias and Fairness: As with any large language model, there is a risk of biases present in the training data being reflected in the model's outputs. Google AI is actively working on mitigating these risks through extensive ethics and safety testing.
• Limited Explainability: The complex nature of the model makes it challenging to fully understand the reasoning behind its outputs.
• Novel Technology: The long-context window feature is still under development, and further research is needed to fully explore its capabilities and limitations.

Disclaimer

Please be advised that this model utilizes wrapped Artificial Intelligence (AI) provided by GCP (the "Vendor"). These AI models may collect, process, and store data as part of their operations. By using our website and accessing these AI models, you hereby consent to the data practices of the Vendor. We do not have control over the data collection, processing, and storage practices of the Vendor. Therefore, we cannot be held responsible or liable for any data handling practices, data loss, or breaches that may occur. It is your responsibility to review the privacy policies and terms of service of the Vendor to understand their data practices. You can access the Vendor's privacy policy and terms of service at https://cloud.google.com/gemini/docs/discover/data-governance. We disclaim all liability with respect to the actions or omissions of the Vendor, and we encourage you to exercise caution and to ensure that you are comfortable with these practices before utilizing the AI models hosted on our site.